Image-guided computer-assisted robotic surgery has become a mainstay in many fields of surgery, particularly in orthopedic surgery. In the pre-operative planning phase of a surgery, representations of the anatomical structure of interest, such as a bone, are generated with medical imaging technologies, such as Computer Tomography (CT), Ultrasound (US) or Magnetic Resonance Imaging (MRI). However, in the intra-operative phase of the surgery, the position of the patient and the anatomical structure may have changed from the position in which the pre-operative representation was generated. In order for image guiding to function properly, the intra-operative position of the anatomical structures must be mapped to the pre-operative representation through a set of transformation parameters in a process called registration.
In current clinical practice this is often achieved by selecting a limited number of precisely located anatomical landmarks or surgically placed marker points, and matching their positions in the pre-operative and intra-operative representations. Because of the criticality of the location of these landmarks, the registration process is time consuming and prone to operator error. Therefore, surface recognition-based based algorithms have been developed, such as the Iterative Closest Point (ICP) and the Unscented Kalman Filter (UKF), which can analyze a large number of data points on a surface to achieve a match, or convergence, between the intra-operatively and pre-operatively generated representations of the surface.
Current clinical practice could be improved by incorporation of these technologies into a reliable, interactive and intra-operative procedure to map the surface of an anatomical structure to a previously generated representation to achieve registration of the surface.